Overview

Brought to you by YData

Dataset statistics

Number of variables54
Number of observations84949
Missing cells142785
Missing cells (%)3.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory139.2 MiB
Average record size in memory1.7 KiB

Variable types

Numeric21
Text13
Categorical19
DateTime1

Alerts

booking_adult_count is highly overall correlated with booking_pax_countHigh correlation
booking_leg_count is highly overall correlated with booking_trip_typeHigh correlation
booking_original_currency is highly overall correlated with preffered_languageHigh correlation
booking_pax_count is highly overall correlated with booking_adult_countHigh correlation
booking_reservation_month is highly overall correlated with leg_departure_monthHigh correlation
booking_segments_count is highly overall correlated with booking_trip_typeHigh correlation
booking_trip_type is highly overall correlated with booking_leg_count and 1 other fieldsHigh correlation
coupon_cabin_class is highly overall correlated with coupon_rbd_codeHigh correlation
coupon_miles is highly overall correlated with coupon_range and 1 other fieldsHigh correlation
coupon_number is highly overall correlated with leg_numberHigh correlation
coupon_range is highly overall correlated with coupon_milesHigh correlation
coupon_rbd_code is highly overall correlated with coupon_cabin_classHigh correlation
graphic_design is highly overall correlated with top_1_section and 2 other fieldsHigh correlation
leg_departure_month is highly overall correlated with booking_reservation_monthHigh correlation
leg_destination_country_code is highly overall correlated with coupon_miles and 1 other fieldsHigh correlation
leg_first_leg_flg is highly overall correlated with leg_last_leg_flg and 1 other fieldsHigh correlation
leg_hours_to_departure is highly overall correlated with leg_origin_country_codeHigh correlation
leg_last_leg_flg is highly overall correlated with leg_first_leg_flg and 1 other fieldsHigh correlation
leg_number is highly overall correlated with coupon_numberHigh correlation
leg_origin_country_code is highly overall correlated with leg_hours_to_departure and 1 other fieldsHigh correlation
leg_stopover_time_h is highly overall correlated with leg_first_leg_flg and 1 other fieldsHigh correlation
preffered_language is highly overall correlated with booking_original_currency and 2 other fieldsHigh correlation
top_1_section is highly overall correlated with graphic_design and 2 other fieldsHigh correlation
top_2_section is highly overall correlated with graphic_design and 1 other fieldsHigh correlation
top_3_section is highly overall correlated with graphic_design and 1 other fieldsHigh correlation
booking_infant_count is highly imbalanced (95.1%) Imbalance
booking_leg_count is highly imbalanced (56.5%) Imbalance
booking_payment_method is highly imbalanced (73.4%) Imbalance
booking_sales_channel is highly imbalanced (54.8%) Imbalance
coupon_cabin_class is highly imbalanced (88.6%) Imbalance
coupon_range is highly imbalanced (62.3%) Imbalance
booking_destination_airport_code has 5199 (6.1%) missing values Missing
booking_original_currency has 7318 (8.6%) missing values Missing
leg_destination_country_code has 65106 (76.6%) missing values Missing
leg_origin_country_code has 65079 (76.6%) missing values Missing
id has unique values Unique
booking_child_count has 81146 (95.5%) zeros Zeros
booking_window_w has 7291 (8.6%) zeros Zeros
leg_duration_h has 3115 (3.7%) zeros Zeros
top_3_section has 16945 (19.9%) zeros Zeros

Reproduction

Analysis started2025-04-12 09:49:12.297520
Analysis finished2025-04-12 09:50:45.717507
Duration1 minute and 33.42 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct84949
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57911.408
Minimum0
Maximum116328
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:50:45.910879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5683.2
Q128606
median57711
Q387172
95-th percentile110589.2
Maximum116328
Range116328
Interquartile range (IQR)58566

Descriptive statistics

Standard deviation33694.182
Coefficient of variation (CV)0.58182288
Kurtosis-1.204811
Mean57911.408
Median Absolute Deviation (MAD)29280
Skewness0.014588137
Sum4.9195162 × 109
Variance1.1352979 × 109
MonotonicityNot monotonic
2025-04-12T11:50:46.136817image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105529 1
 
< 0.1%
85092 1
 
< 0.1%
58605 1
 
< 0.1%
26759 1
 
< 0.1%
15674 1
 
< 0.1%
64692 1
 
< 0.1%
78039 1
 
< 0.1%
115584 1
 
< 0.1%
22480 1
 
< 0.1%
109814 1
 
< 0.1%
Other values (84939) 84939
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
ValueCountFrequency (%)
116328 1
< 0.1%
116327 1
< 0.1%
116326 1
< 0.1%
116325 1
< 0.1%
116324 1
< 0.1%
116320 1
< 0.1%
116319 1
< 0.1%
116317 1
< 0.1%
116314 1
< 0.1%
116313 1
< 0.1%

booking_adult_count
Real number (ℝ)

High correlation 

Distinct49
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4624775
Minimum0
Maximum96
Zeros34
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:50:46.354932image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum96
Range96
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4794811
Coefficient of variation (CV)1.0116266
Kurtosis636.44277
Mean1.4624775
Median Absolute Deviation (MAD)0
Skewness18.30858
Sum124236
Variance2.1888643
MonotonicityNot monotonic
2025-04-12T11:50:46.591073image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1 59936
70.6%
2 19367
 
22.8%
3 3038
 
3.6%
4 1444
 
1.7%
5 391
 
0.5%
6 209
 
0.2%
7 117
 
0.1%
8 104
 
0.1%
9 57
 
0.1%
0 34
 
< 0.1%
Other values (39) 252
 
0.3%
ValueCountFrequency (%)
0 34
 
< 0.1%
1 59936
70.6%
2 19367
 
22.8%
3 3038
 
3.6%
4 1444
 
1.7%
5 391
 
0.5%
6 209
 
0.2%
7 117
 
0.1%
8 104
 
0.1%
9 57
 
0.1%
ValueCountFrequency (%)
96 1
 
< 0.1%
92 1
 
< 0.1%
55 1
 
< 0.1%
53 1
 
< 0.1%
50 1
 
< 0.1%
49 1
 
< 0.1%
47 1
 
< 0.1%
45 3
< 0.1%
44 2
< 0.1%
43 2
< 0.1%

booking_child_count
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.057975962
Minimum0
Maximum5
Zeros81146
Zeros (%)95.5%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:50:46.761997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.29031121
Coefficient of variation (CV)5.007441
Kurtosis39.393484
Mean0.057975962
Median Absolute Deviation (MAD)0
Skewness5.8444326
Sum4925
Variance0.0842806
MonotonicityNot monotonic
2025-04-12T11:50:46.913618image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 81146
95.5%
1 2805
 
3.3%
2 886
 
1.0%
3 102
 
0.1%
4 8
 
< 0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
0 81146
95.5%
1 2805
 
3.3%
2 886
 
1.0%
3 102
 
0.1%
4 8
 
< 0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
4 8
 
< 0.1%
3 102
 
0.1%
2 886
 
1.0%
1 2805
 
3.3%
0 81146
95.5%
Distinct88
Distinct (%)0.1%
Missing5199
Missing (%)6.1%
Memory size4.1 MiB
2025-04-12T11:50:47.270681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters239250
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTBS
2nd rowWAW
3rd rowBEG
4th rowBOM
5th rowOTP
ValueCountFrequency (%)
waw 21676
27.2%
lhr 2424
 
3.0%
ams 1941
 
2.4%
vno 1857
 
2.3%
jfk 1771
 
2.2%
ist 1726
 
2.2%
krk 1726
 
2.2%
ord 1710
 
2.1%
cdg 1541
 
1.9%
prg 1489
 
1.9%
Other values (78) 41889
52.5%
2025-04-12T11:50:47.792372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 44798
18.7%
A 32729
13.7%
R 17183
 
7.2%
O 10174
 
4.3%
L 10011
 
4.2%
T 9441
 
3.9%
D 9298
 
3.9%
C 8940
 
3.7%
S 8728
 
3.6%
N 7938
 
3.3%
Other values (16) 80010
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 44798
18.7%
A 32729
13.7%
R 17183
 
7.2%
O 10174
 
4.3%
L 10011
 
4.2%
T 9441
 
3.9%
D 9298
 
3.9%
C 8940
 
3.7%
S 8728
 
3.6%
N 7938
 
3.3%
Other values (16) 80010
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 44798
18.7%
A 32729
13.7%
R 17183
 
7.2%
O 10174
 
4.3%
L 10011
 
4.2%
T 9441
 
3.9%
D 9298
 
3.9%
C 8940
 
3.7%
S 8728
 
3.6%
N 7938
 
3.3%
Other values (16) 80010
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 44798
18.7%
A 32729
13.7%
R 17183
 
7.2%
O 10174
 
4.3%
L 10011
 
4.2%
T 9441
 
3.9%
D 9298
 
3.9%
C 8940
 
3.7%
S 8728
 
3.6%
N 7938
 
3.3%
Other values (16) 80010
33.4%

booking_id
Real number (ℝ)

Distinct81734
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5503856
Minimum1000143
Maximum9999956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:50:48.002676image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1000143
5-th percentile1459341.4
Q13251179
median5503001
Q37740747
95-th percentile9557143.2
Maximum9999956
Range8999813
Interquartile range (IQR)4489568

Descriptive statistics

Standard deviation2593980.4
Coefficient of variation (CV)0.47130237
Kurtosis-1.1946804
Mean5503856
Median Absolute Deviation (MAD)2244662
Skewness-0.00042049661
Sum4.6754706 × 1011
Variance6.7287343 × 1012
MonotonicityNot monotonic
2025-04-12T11:50:48.192202image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5567317 11
 
< 0.1%
7494700 7
 
< 0.1%
5709675 6
 
< 0.1%
2434140 5
 
< 0.1%
2198660 5
 
< 0.1%
4075744 5
 
< 0.1%
2057360 5
 
< 0.1%
1817985 5
 
< 0.1%
5837515 4
 
< 0.1%
5534177 4
 
< 0.1%
Other values (81724) 84892
99.9%
ValueCountFrequency (%)
1000143 1
< 0.1%
1000588 1
< 0.1%
1000605 2
< 0.1%
1000843 1
< 0.1%
1000848 1
< 0.1%
1000860 2
< 0.1%
1000899 1
< 0.1%
1001098 1
< 0.1%
1001137 1
< 0.1%
1001169 1
< 0.1%
ValueCountFrequency (%)
9999956 1
< 0.1%
9999704 1
< 0.1%
9999596 1
< 0.1%
9999457 1
< 0.1%
9999335 1
< 0.1%
9999264 1
< 0.1%
9999220 1
< 0.1%
9999172 1
< 0.1%
9999053 1
< 0.1%
9999041 1
< 0.1%

booking_infant_count
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
0
83907 
1
 
1022
2
 
19
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters84949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 83907
98.8%
1 1022
 
1.2%
2 19
 
< 0.1%
4 1
 
< 0.1%

Length

2025-04-12T11:50:48.654907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T11:50:48.835089image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 83907
98.8%
1 1022
 
1.2%
2 19
 
< 0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 83907
98.8%
1 1022
 
1.2%
2 19
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 83907
98.8%
1 1022
 
1.2%
2 19
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 83907
98.8%
1 1022
 
1.2%
2 19
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 83907
98.8%
1 1022
 
1.2%
2 19
 
< 0.1%
4 1
 
< 0.1%

booking_leg_count
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2
61847 
1
22840 
3
 
252
4
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters84949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 61847
72.8%
1 22840
 
26.9%
3 252
 
0.3%
4 10
 
< 0.1%

Length

2025-04-12T11:50:49.002391image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T11:50:49.160328image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 61847
72.8%
1 22840
 
26.9%
3 252
 
0.3%
4 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 61847
72.8%
1 22840
 
26.9%
3 252
 
0.3%
4 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 61847
72.8%
1 22840
 
26.9%
3 252
 
0.3%
4 10
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 61847
72.8%
1 22840
 
26.9%
3 252
 
0.3%
4 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 61847
72.8%
1 22840
 
26.9%
3 252
 
0.3%
4 10
 
< 0.1%
Distinct72
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2025-04-12T11:50:49.419719image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters169898
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowGE
2nd rowUS
3rd rowPL
4th rowSE
5th rowPL
ValueCountFrequency (%)
pl 30864
36.3%
us 10015
 
11.8%
se 9310
 
11.0%
gb 3410
 
4.0%
de 3067
 
3.6%
ca 2491
 
2.9%
lt 2349
 
2.8%
nl 1700
 
2.0%
ee 1625
 
1.9%
dk 1605
 
1.9%
Other values (62) 18513
21.8%
2025-04-12T11:50:49.839190image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 36334
21.4%
P 31131
18.3%
S 21680
12.8%
E 18932
11.1%
U 11701
 
6.9%
B 5125
 
3.0%
D 4999
 
2.9%
C 4902
 
2.9%
R 4675
 
2.8%
G 4440
 
2.6%
Other values (15) 25979
15.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 169898
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 36334
21.4%
P 31131
18.3%
S 21680
12.8%
E 18932
11.1%
U 11701
 
6.9%
B 5125
 
3.0%
D 4999
 
2.9%
C 4902
 
2.9%
R 4675
 
2.8%
G 4440
 
2.6%
Other values (15) 25979
15.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 169898
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 36334
21.4%
P 31131
18.3%
S 21680
12.8%
E 18932
11.1%
U 11701
 
6.9%
B 5125
 
3.0%
D 4999
 
2.9%
C 4902
 
2.9%
R 4675
 
2.8%
G 4440
 
2.6%
Other values (15) 25979
15.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 169898
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 36334
21.4%
P 31131
18.3%
S 21680
12.8%
E 18932
11.1%
U 11701
 
6.9%
B 5125
 
3.0%
D 4999
 
2.9%
C 4902
 
2.9%
R 4675
 
2.8%
G 4440
 
2.6%
Other values (15) 25979
15.3%
Distinct86
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-04-12T11:50:50.204689image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters254847
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWAW
2nd rowEWR
3rd rowSZZ
4th rowWAW
5th rowOSL
ValueCountFrequency (%)
waw 32282
38.0%
vno 2980
 
3.5%
lhr 2669
 
3.1%
ord 2179
 
2.6%
jfk 1916
 
2.3%
krk 1852
 
2.2%
yyz 1852
 
2.2%
ams 1822
 
2.1%
tll 1744
 
2.1%
otp 1643
 
1.9%
Other values (76) 34010
40.0%
2025-04-12T11:50:50.713253image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 66876
26.2%
A 40456
15.9%
R 18368
 
7.2%
O 12353
 
4.8%
L 11155
 
4.4%
D 7611
 
3.0%
S 7424
 
2.9%
N 7184
 
2.8%
T 7137
 
2.8%
K 6454
 
2.5%
Other values (16) 69829
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 254847
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 66876
26.2%
A 40456
15.9%
R 18368
 
7.2%
O 12353
 
4.8%
L 11155
 
4.4%
D 7611
 
3.0%
S 7424
 
2.9%
N 7184
 
2.8%
T 7137
 
2.8%
K 6454
 
2.5%
Other values (16) 69829
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 254847
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 66876
26.2%
A 40456
15.9%
R 18368
 
7.2%
O 12353
 
4.8%
L 11155
 
4.4%
D 7611
 
3.0%
S 7424
 
2.9%
N 7184
 
2.8%
T 7137
 
2.8%
K 6454
 
2.5%
Other values (16) 69829
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 254847
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 66876
26.2%
A 40456
15.9%
R 18368
 
7.2%
O 12353
 
4.8%
L 11155
 
4.4%
D 7611
 
3.0%
S 7424
 
2.9%
N 7184
 
2.8%
T 7137
 
2.8%
K 6454
 
2.5%
Other values (16) 69829
27.4%

booking_original_currency
Categorical

High correlation  Missing 

Distinct42
Distinct (%)0.1%
Missing7318
Missing (%)8.6%
Memory size4.2 MiB
PLN
28679 
EUR
16648 
USD
9916 
SEK
8352 
GBP
3105 
Other values (37)
10931 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters232893
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowGEL
2nd rowUSD
3rd rowPLN
4th rowPLN
5th rowPLN

Common Values

ValueCountFrequency (%)
PLN 28679
33.8%
EUR 16648
19.6%
USD 9916
 
11.7%
SEK 8352
 
9.8%
GBP 3105
 
3.7%
CAD 2297
 
2.7%
DKK 1477
 
1.7%
NOK 1150
 
1.4%
CHF 1069
 
1.3%
CZK 991
 
1.2%
Other values (32) 3947
 
4.6%
(Missing) 7318
 
8.6%

Length

2025-04-12T11:50:50.911950image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pln 28679
36.9%
eur 16648
21.4%
usd 9916
 
12.8%
sek 8352
 
10.8%
gbp 3105
 
4.0%
cad 2297
 
3.0%
dkk 1477
 
1.9%
nok 1150
 
1.5%
chf 1069
 
1.4%
czk 991
 
1.3%
Other values (32) 3947
 
5.1%

Most occurring characters

ValueCountFrequency (%)
P 32072
13.8%
N 31084
13.3%
L 29029
12.5%
U 27189
11.7%
E 25503
11.0%
S 18647
8.0%
R 18535
8.0%
K 14117
6.1%
D 14027
6.0%
C 4380
 
1.9%
Other values (15) 18310
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 232893
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 32072
13.8%
N 31084
13.3%
L 29029
12.5%
U 27189
11.7%
E 25503
11.0%
S 18647
8.0%
R 18535
8.0%
K 14117
6.1%
D 14027
6.0%
C 4380
 
1.9%
Other values (15) 18310
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 232893
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 32072
13.8%
N 31084
13.3%
L 29029
12.5%
U 27189
11.7%
E 25503
11.0%
S 18647
8.0%
R 18535
8.0%
K 14117
6.1%
D 14027
6.0%
C 4380
 
1.9%
Other values (15) 18310
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 232893
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 32072
13.8%
N 31084
13.3%
L 29029
12.5%
U 27189
11.7%
E 25503
11.0%
S 18647
8.0%
R 18535
8.0%
K 14117
6.1%
D 14027
6.0%
C 4380
 
1.9%
Other values (15) 18310
7.9%

booking_pax_count
Real number (ℝ)

High correlation 

Distinct49
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5329786
Minimum1
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:50:51.098592image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum96
Range95
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.53779
Coefficient of variation (CV)1.0031386
Kurtosis555.78944
Mean1.5329786
Median Absolute Deviation (MAD)0
Skewness16.521934
Sum130225
Variance2.364798
MonotonicityNot monotonic
2025-04-12T11:50:51.304855image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1 58414
68.8%
2 18403
 
21.7%
3 4247
 
5.0%
4 2410
 
2.8%
5 611
 
0.7%
6 284
 
0.3%
7 142
 
0.2%
8 115
 
0.1%
9 69
 
0.1%
10 27
 
< 0.1%
Other values (39) 227
 
0.3%
ValueCountFrequency (%)
1 58414
68.8%
2 18403
 
21.7%
3 4247
 
5.0%
4 2410
 
2.8%
5 611
 
0.7%
6 284
 
0.3%
7 142
 
0.2%
8 115
 
0.1%
9 69
 
0.1%
10 27
 
< 0.1%
ValueCountFrequency (%)
96 1
 
< 0.1%
94 1
 
< 0.1%
55 1
 
< 0.1%
53 1
 
< 0.1%
50 1
 
< 0.1%
49 1
 
< 0.1%
47 1
 
< 0.1%
45 3
< 0.1%
44 2
< 0.1%
43 2
< 0.1%

booking_payment_method
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
95582540167d4ebb9497a7031f046757
76491 
1bb2b25a099d4a438425d276203a0c2c
 
7087
e248b21630a9467cbdff5232836930a3
 
1364
236d48d3e6ce425599faccbf2f0a06f2
 
7

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters2718368
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row95582540167d4ebb9497a7031f046757
2nd row95582540167d4ebb9497a7031f046757
3rd row1bb2b25a099d4a438425d276203a0c2c
4th row1bb2b25a099d4a438425d276203a0c2c
5th row95582540167d4ebb9497a7031f046757

Common Values

ValueCountFrequency (%)
95582540167d4ebb9497a7031f046757 76491
90.0%
1bb2b25a099d4a438425d276203a0c2c 7087
 
8.3%
e248b21630a9467cbdff5232836930a3 1364
 
1.6%
236d48d3e6ce425599faccbf2f0a06f2 7
 
< 0.1%

Length

2025-04-12T11:50:51.480688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T11:50:51.628795image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
95582540167d4ebb9497a7031f046757 76491
90.0%
1bb2b25a099d4a438425d276203a0c2c 7087
 
8.3%
e248b21630a9467cbdff5232836930a3 1364
 
1.6%
236d48d3e6ce425599faccbf2f0a06f2 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
7 390906
14.4%
4 329967
12.1%
5 321516
11.8%
0 253476
9.3%
9 246389
9.1%
b 176978
 
6.5%
6 164182
 
6.0%
1 161433
 
5.9%
2 124497
 
4.6%
a 100494
 
3.7%
Other values (6) 448530
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2718368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 390906
14.4%
4 329967
12.1%
5 321516
11.8%
0 253476
9.3%
9 246389
9.1%
b 176978
 
6.5%
6 164182
 
6.0%
1 161433
 
5.9%
2 124497
 
4.6%
a 100494
 
3.7%
Other values (6) 448530
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2718368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 390906
14.4%
4 329967
12.1%
5 321516
11.8%
0 253476
9.3%
9 246389
9.1%
b 176978
 
6.5%
6 164182
 
6.0%
1 161433
 
5.9%
2 124497
 
4.6%
a 100494
 
3.7%
Other values (6) 448530
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2718368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 390906
14.4%
4 329967
12.1%
5 321516
11.8%
0 253476
9.3%
9 246389
9.1%
b 176978
 
6.5%
6 164182
 
6.0%
1 161433
 
5.9%
2 124497
 
4.6%
a 100494
 
3.7%
Other values (6) 448530
16.5%

booking_reservation_month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6554756
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:50:51.763427image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median9
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.1448372
Coefficient of variation (CV)0.41079579
Kurtosis-0.13013452
Mean7.6554756
Median Absolute Deviation (MAD)1
Skewness-0.94006014
Sum650325
Variance9.890001
MonotonicityNot monotonic
2025-04-12T11:50:51.865232image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 17694
20.8%
8 14115
16.6%
10 13381
15.8%
1 7670
9.0%
7 7363
8.7%
11 7118
8.4%
12 4786
 
5.6%
2 4612
 
5.4%
6 3266
 
3.8%
5 2139
 
2.5%
Other values (2) 2805
 
3.3%
ValueCountFrequency (%)
1 7670
9.0%
2 4612
 
5.4%
3 1327
 
1.6%
4 1478
 
1.7%
5 2139
 
2.5%
6 3266
 
3.8%
7 7363
8.7%
8 14115
16.6%
9 17694
20.8%
10 13381
15.8%
ValueCountFrequency (%)
12 4786
 
5.6%
11 7118
8.4%
10 13381
15.8%
9 17694
20.8%
8 14115
16.6%
7 7363
8.7%
6 3266
 
3.8%
5 2139
 
2.5%
4 1478
 
1.7%
3 1327
 
1.6%

booking_sales_channel
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
website
57710 
agents
25366 
call_center
 
1088
aiport_agents
 
644
internal_agents
 
141

Length

Max length15
Median length7
Mean length6.8113927
Min length6

Characters and Unicode

Total characters578621
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwebsite
2nd rowwebsite
3rd rowwebsite
4th rowagents
5th rowagents

Common Values

ValueCountFrequency (%)
website 57710
67.9%
agents 25366
29.9%
call_center 1088
 
1.3%
aiport_agents 644
 
0.8%
internal_agents 141
 
0.2%

Length

2025-04-12T11:50:51.984086image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T11:50:52.085613image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
website 57710
67.9%
agents 25366
29.9%
call_center 1088
 
1.3%
aiport_agents 644
 
0.8%
internal_agents 141
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 143888
24.9%
t 85734
14.8%
s 83861
14.5%
i 58495
10.1%
w 57710
10.0%
b 57710
10.0%
a 28024
 
4.8%
n 27521
 
4.8%
g 26151
 
4.5%
l 2317
 
0.4%
Other values (5) 7210
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 578621
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 143888
24.9%
t 85734
14.8%
s 83861
14.5%
i 58495
10.1%
w 57710
10.0%
b 57710
10.0%
a 28024
 
4.8%
n 27521
 
4.8%
g 26151
 
4.5%
l 2317
 
0.4%
Other values (5) 7210
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 578621
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 143888
24.9%
t 85734
14.8%
s 83861
14.5%
i 58495
10.1%
w 57710
10.0%
b 57710
10.0%
a 28024
 
4.8%
n 27521
 
4.8%
g 26151
 
4.5%
l 2317
 
0.4%
Other values (5) 7210
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 578621
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 143888
24.9%
t 85734
14.8%
s 83861
14.5%
i 58495
10.1%
w 57710
10.0%
b 57710
10.0%
a 28024
 
4.8%
n 27521
 
4.8%
g 26151
 
4.5%
l 2317
 
0.4%
Other values (5) 7210
 
1.2%

booking_segments_count
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2488905
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:50:52.200999image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.95216071
Coefficient of variation (CV)0.42339132
Kurtosis-0.23977367
Mean2.2488905
Median Absolute Deviation (MAD)0
Skewness0.84027966
Sum191041
Variance0.90661002
MonotonicityNot monotonic
2025-04-12T11:50:52.295535image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 51996
61.2%
4 16268
 
19.2%
1 14101
 
16.6%
3 2526
 
3.0%
5 50
 
0.1%
6 8
 
< 0.1%
ValueCountFrequency (%)
1 14101
 
16.6%
2 51996
61.2%
3 2526
 
3.0%
4 16268
 
19.2%
5 50
 
0.1%
6 8
 
< 0.1%
ValueCountFrequency (%)
6 8
 
< 0.1%
5 50
 
0.1%
4 16268
 
19.2%
3 2526
 
3.0%
2 51996
61.2%
1 14101
 
16.6%

booking_trip_type
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
RT
58787 
OW
20963 
MC
 
5199

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters169898
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRT
2nd rowRT
3rd rowRT
4th rowOW
5th rowRT

Common Values

ValueCountFrequency (%)
RT 58787
69.2%
OW 20963
 
24.7%
MC 5199
 
6.1%

Length

2025-04-12T11:50:52.419783image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T11:50:52.543135image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
rt 58787
69.2%
ow 20963
 
24.7%
mc 5199
 
6.1%

Most occurring characters

ValueCountFrequency (%)
R 58787
34.6%
T 58787
34.6%
O 20963
 
12.3%
W 20963
 
12.3%
M 5199
 
3.1%
C 5199
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 169898
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 58787
34.6%
T 58787
34.6%
O 20963
 
12.3%
W 20963
 
12.3%
M 5199
 
3.1%
C 5199
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 169898
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 58787
34.6%
T 58787
34.6%
O 20963
 
12.3%
W 20963
 
12.3%
M 5199
 
3.1%
C 5199
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 169898
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 58787
34.6%
T 58787
34.6%
O 20963
 
12.3%
W 20963
 
12.3%
M 5199
 
3.1%
C 5199
 
3.1%

booking_window_w
Real number (ℝ)

Zeros 

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.915667
Minimum0
Maximum53
Zeros7291
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:50:52.691916image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile20
Maximum53
Range53
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.9075653
Coefficient of variation (CV)1.1676731
Kurtosis8.1100139
Mean5.915667
Median Absolute Deviation (MAD)3
Skewness2.5357693
Sum502530
Variance47.714459
MonotonicityNot monotonic
2025-04-12T11:50:52.828376image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12756
15.0%
2 11212
13.2%
3 9535
11.2%
4 7918
9.3%
0 7291
8.6%
5 6175
7.3%
6 5102
 
6.0%
7 4012
 
4.7%
8 3370
 
4.0%
9 2490
 
2.9%
Other values (43) 15088
17.8%
ValueCountFrequency (%)
0 7291
8.6%
1 12756
15.0%
2 11212
13.2%
3 9535
11.2%
4 7918
9.3%
5 6175
7.3%
6 5102
 
6.0%
7 4012
 
4.7%
8 3370
 
4.0%
9 2490
 
2.9%
ValueCountFrequency (%)
53 2
 
< 0.1%
51 5
 
< 0.1%
50 15
 
< 0.1%
49 42
< 0.1%
48 44
0.1%
47 31
< 0.1%
46 45
0.1%
45 22
< 0.1%
44 28
< 0.1%
43 34
< 0.1%

coupon_cabin_class
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
economy
83648 
premium
 
1301

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters594643
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roweconomy
2nd roweconomy
3rd roweconomy
4th roweconomy
5th roweconomy

Common Values

ValueCountFrequency (%)
economy 83648
98.5%
premium 1301
 
1.5%

Length

2025-04-12T11:50:52.946984image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T11:50:53.039874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
economy 83648
98.5%
premium 1301
 
1.5%

Most occurring characters

ValueCountFrequency (%)
o 167296
28.1%
m 86250
14.5%
e 84949
14.3%
c 83648
14.1%
n 83648
14.1%
y 83648
14.1%
p 1301
 
0.2%
r 1301
 
0.2%
i 1301
 
0.2%
u 1301
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 594643
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 167296
28.1%
m 86250
14.5%
e 84949
14.3%
c 83648
14.1%
n 83648
14.1%
y 83648
14.1%
p 1301
 
0.2%
r 1301
 
0.2%
i 1301
 
0.2%
u 1301
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 594643
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 167296
28.1%
m 86250
14.5%
e 84949
14.3%
c 83648
14.1%
n 83648
14.1%
y 83648
14.1%
p 1301
 
0.2%
r 1301
 
0.2%
i 1301
 
0.2%
u 1301
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 594643
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 167296
28.1%
m 86250
14.5%
e 84949
14.3%
c 83648
14.1%
n 83648
14.1%
y 83648
14.1%
p 1301
 
0.2%
r 1301
 
0.2%
i 1301
 
0.2%
u 1301
 
0.2%
Distinct82
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-04-12T11:50:53.350296image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters254847
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowWAW
2nd rowEWR
3rd rowWAW
4th rowBOM
5th rowWAW
ValueCountFrequency (%)
waw 40154
47.3%
lhr 2429
 
2.9%
ord 1934
 
2.3%
jfk 1857
 
2.2%
ams 1806
 
2.1%
vno 1589
 
1.9%
yyz 1480
 
1.7%
bru 1220
 
1.4%
cdg 1168
 
1.4%
otp 1120
 
1.3%
Other values (72) 30192
35.5%
2025-04-12T11:50:53.859479image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 81332
31.9%
A 48754
19.1%
R 13997
 
5.5%
L 8562
 
3.4%
O 8102
 
3.2%
D 7134
 
2.8%
C 6734
 
2.6%
S 6724
 
2.6%
T 6259
 
2.5%
H 5687
 
2.2%
Other values (16) 61562
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 254847
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 81332
31.9%
A 48754
19.1%
R 13997
 
5.5%
L 8562
 
3.4%
O 8102
 
3.2%
D 7134
 
2.8%
C 6734
 
2.6%
S 6724
 
2.6%
T 6259
 
2.5%
H 5687
 
2.2%
Other values (16) 61562
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 254847
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 81332
31.9%
A 48754
19.1%
R 13997
 
5.5%
L 8562
 
3.4%
O 8102
 
3.2%
D 7134
 
2.8%
C 6734
 
2.6%
S 6724
 
2.6%
T 6259
 
2.5%
H 5687
 
2.2%
Other values (16) 61562
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 254847
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 81332
31.9%
A 48754
19.1%
R 13997
 
5.5%
L 8562
 
3.4%
O 8102
 
3.2%
D 7134
 
2.8%
C 6734
 
2.6%
S 6724
 
2.6%
T 6259
 
2.5%
H 5687
 
2.2%
Other values (16) 61562
24.2%
Distinct4571
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
2025-04-12T11:50:54.243285image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters2718368
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1412 ?
Unique (%)1.7%

Sample

1st rowee702af56c9f42c49677ede02047afcd
2nd row64e30cc1470248f6896a74d5774954b8
3rd row76c8fafae85c47e0849a2b85b23dce08
4th row9915e38b5cf247588f6f4ae680b53f5e
5th row0b93a67a450640c19f68cd8e77464ffa
ValueCountFrequency (%)
d576b543eb25443ba0540b652e0d31cb 1384
 
1.6%
b5495dec57b448b1a7448671d1da2275 1038
 
1.2%
382dd7483da444d2841eb36023ee0358 880
 
1.0%
e48ca0799a804a5ba3b361447ad4d602 792
 
0.9%
4dd0a4d3f47c4a9b966440cc2277b291 791
 
0.9%
1ff1ed3c2acd4cea9c10b09878cd63fb 682
 
0.8%
99a5ac38c0fb495fa74a1a803fca8435 674
 
0.8%
ee702af56c9f42c49677ede02047afcd 655
 
0.8%
f484b44923e0489eb8582caf5c0b2978 624
 
0.7%
82213c96be824c9ea6f47d5ae8776bac 618
 
0.7%
Other values (4561) 76811
90.4%
2025-04-12T11:50:54.837105image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 257726
 
9.5%
8 182730
 
6.7%
b 178570
 
6.6%
a 176546
 
6.5%
9 173358
 
6.4%
3 165267
 
6.1%
7 163434
 
6.0%
c 163140
 
6.0%
5 163003
 
6.0%
d 162461
 
6.0%
Other values (6) 932133
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2718368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 257726
 
9.5%
8 182730
 
6.7%
b 178570
 
6.6%
a 176546
 
6.5%
9 173358
 
6.4%
3 165267
 
6.1%
7 163434
 
6.0%
c 163140
 
6.0%
5 163003
 
6.0%
d 162461
 
6.0%
Other values (6) 932133
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2718368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 257726
 
9.5%
8 182730
 
6.7%
b 178570
 
6.6%
a 176546
 
6.5%
9 173358
 
6.4%
3 165267
 
6.1%
7 163434
 
6.0%
c 163140
 
6.0%
5 163003
 
6.0%
d 162461
 
6.0%
Other values (6) 932133
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2718368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 257726
 
9.5%
8 182730
 
6.7%
b 178570
 
6.6%
a 176546
 
6.5%
9 173358
 
6.4%
3 165267
 
6.1%
7 163434
 
6.0%
c 163140
 
6.0%
5 163003
 
6.0%
d 162461
 
6.0%
Other values (6) 932133
34.3%
Distinct378
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5308328.6
Minimum1009781
Maximum9989525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:50:55.053582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1009781
5-th percentile1327210
Q13218088
median5296004
Q37621582
95-th percentile9342807
Maximum9989525
Range8979744
Interquartile range (IQR)4403494

Descriptive statistics

Standard deviation2544544.6
Coefficient of variation (CV)0.47934948
Kurtosis-1.1807325
Mean5308328.6
Median Absolute Deviation (MAD)2186885
Skewness0.01907752
Sum4.5093721 × 1011
Variance6.4747071 × 1012
MonotonicityNot monotonic
2025-04-12T11:50:55.250881image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1327210 1374
 
1.6%
2609995 1201
 
1.4%
7648128 1197
 
1.4%
1487938 1030
 
1.2%
5609032 999
 
1.2%
4286531 961
 
1.1%
3297471 921
 
1.1%
7127217 887
 
1.0%
4547752 798
 
0.9%
1299755 795
 
0.9%
Other values (368) 74786
88.0%
ValueCountFrequency (%)
1009781 65
 
0.1%
1156936 473
0.6%
1158981 10
 
< 0.1%
1160696 157
 
0.2%
1208037 85
 
0.1%
1236303 188
 
0.2%
1252531 316
0.4%
1254679 239
0.3%
1265721 152
 
0.2%
1271872 388
0.5%
ValueCountFrequency (%)
9989525 273
0.3%
9959105 253
0.3%
9951897 95
 
0.1%
9950251 248
0.3%
9949418 421
0.5%
9938466 106
 
0.1%
9903518 1
 
< 0.1%
9868778 116
 
0.1%
9850913 66
 
0.1%
9675329 408
0.5%

coupon_miles
Real number (ℝ)

High correlation 

Distinct83
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1545.8341
Minimum-9999
Maximum6003
Zeros0
Zeros (%)0.0%
Negative132
Negative (%)0.2%
Memory size663.8 KiB
2025-04-12T11:50:55.434121image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile316
Q1531
median714
Q31588
95-th percentile4801
Maximum6003
Range16002
Interquartile range (IQR)1057

Descriptive statistics

Standard deviation1708.4085
Coefficient of variation (CV)1.1051694
Kurtosis3.0896876
Mean1545.8341
Median Absolute Deviation (MAD)232
Skewness0.77299674
Sum1.3131706 × 108
Variance2918659.6
MonotonicityNot monotonic
2025-04-12T11:50:55.669449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
912 4743
 
5.6%
4255 3409
 
4.0%
685 3336
 
3.9%
4309 2736
 
3.2%
4673 2666
 
3.1%
246 2549
 
3.0%
712 2416
 
2.8%
574 2271
 
2.7%
835 2207
 
2.6%
323 2169
 
2.6%
Other values (73) 56447
66.4%
ValueCountFrequency (%)
-9999 132
 
0.2%
211 131
 
0.2%
242 311
 
0.4%
246 2549
3.0%
316 1146
1.3%
323 2169
2.6%
334 1377
1.6%
341 897
 
1.1%
350 1109
1.3%
356 116
 
0.1%
ValueCountFrequency (%)
6003 1392
1.6%
5353 1118
1.3%
5276 1230
1.4%
5056 309
 
0.4%
4985 164
 
0.2%
4801 881
 
1.0%
4726 899
 
1.1%
4673 2666
3.1%
4381 188
 
0.2%
4315 112
 
0.1%

coupon_number
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6027734
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:50:55.825238image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81292754
Coefficient of variation (CV)0.50720054
Kurtosis1.798903
Mean1.6027734
Median Absolute Deviation (MAD)0
Skewness1.4466719
Sum136154
Variance0.66085119
MonotonicityNot monotonic
2025-04-12T11:50:55.976376image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 47195
55.6%
2 28624
33.7%
3 4921
 
5.8%
4 4112
 
4.8%
5 82
 
0.1%
6 15
 
< 0.1%
ValueCountFrequency (%)
1 47195
55.6%
2 28624
33.7%
3 4921
 
5.8%
4 4112
 
4.8%
5 82
 
0.1%
6 15
 
< 0.1%
ValueCountFrequency (%)
6 15
 
< 0.1%
5 82
 
0.1%
4 4112
 
4.8%
3 4921
 
5.8%
2 28624
33.7%
1 47195
55.6%
Distinct80
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-04-12T11:50:56.284272image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters254847
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTBS
2nd rowWAW
3rd rowBEG
4th rowWAW
5th rowOSL
ValueCountFrequency (%)
waw 41416
48.8%
lhr 2314
 
2.7%
vno 1692
 
2.0%
ord 1634
 
1.9%
jfk 1558
 
1.8%
ams 1530
 
1.8%
yyz 1264
 
1.5%
bru 1196
 
1.4%
otp 1152
 
1.4%
prg 1084
 
1.3%
Other values (70) 30109
35.4%
2025-04-12T11:50:56.788429image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 83753
32.9%
A 49490
19.4%
R 13642
 
5.4%
L 8637
 
3.4%
O 8211
 
3.2%
S 6994
 
2.7%
D 6716
 
2.6%
T 6489
 
2.5%
C 6203
 
2.4%
N 5486
 
2.2%
Other values (16) 59226
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 254847
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 83753
32.9%
A 49490
19.4%
R 13642
 
5.4%
L 8637
 
3.4%
O 8211
 
3.2%
S 6994
 
2.7%
D 6716
 
2.6%
T 6489
 
2.5%
C 6203
 
2.4%
N 5486
 
2.2%
Other values (16) 59226
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 254847
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 83753
32.9%
A 49490
19.4%
R 13642
 
5.4%
L 8637
 
3.4%
O 8211
 
3.2%
S 6994
 
2.7%
D 6716
 
2.6%
T 6489
 
2.5%
C 6203
 
2.4%
N 5486
 
2.2%
Other values (16) 59226
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 254847
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 83753
32.9%
A 49490
19.4%
R 13642
 
5.4%
L 8637
 
3.4%
O 8211
 
3.2%
S 6994
 
2.7%
D 6716
 
2.6%
T 6489
 
2.5%
C 6203
 
2.4%
N 5486
 
2.2%
Other values (16) 59226
23.2%

coupon_range
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing83
Missing (%)0.1%
Memory size4.1 MiB
SH
66652 
LH
18194 
MH
 
16
DOM
 
4

Length

Max length3
Median length2
Mean length2.0000471
Min length2

Characters and Unicode

Total characters169736
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSH
2nd rowLH
3rd rowSH
4th rowLH
5th rowSH

Common Values

ValueCountFrequency (%)
SH 66652
78.5%
LH 18194
 
21.4%
MH 16
 
< 0.1%
DOM 4
 
< 0.1%
(Missing) 83
 
0.1%

Length

2025-04-12T11:50:56.975931image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T11:50:57.129841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
sh 66652
78.5%
lh 18194
 
21.4%
mh 16
 
< 0.1%
dom 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
H 84862
50.0%
S 66652
39.3%
L 18194
 
10.7%
M 20
 
< 0.1%
D 4
 
< 0.1%
O 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 169736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
H 84862
50.0%
S 66652
39.3%
L 18194
 
10.7%
M 20
 
< 0.1%
D 4
 
< 0.1%
O 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 169736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
H 84862
50.0%
S 66652
39.3%
L 18194
 
10.7%
M 20
 
< 0.1%
D 4
 
< 0.1%
O 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 169736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
H 84862
50.0%
S 66652
39.3%
L 18194
 
10.7%
M 20
 
< 0.1%
D 4
 
< 0.1%
O 4
 
< 0.1%

coupon_rbd_code
Categorical

High correlation 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
05aa66ad90564e9caf593f69bf0df44c
12620 
06461e5ef0bb4941995dadba33f4b58a
12535 
5b4d18c017084084b2c8e3860625237e
12354 
b6460aef6f204ba8940329aa9f7f99cd
10914 
22da46f1ddc1406e8e6aa78cdf573c16
9298 
Other values (19)
27228 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters2718368
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowb6460aef6f204ba8940329aa9f7f99cd
2nd row5b4d18c017084084b2c8e3860625237e
3rd rowfcd7f2fe3e034355bcc3fff350b5c928
4th row06461e5ef0bb4941995dadba33f4b58a
5th rowfcd7f2fe3e034355bcc3fff350b5c928

Common Values

ValueCountFrequency (%)
05aa66ad90564e9caf593f69bf0df44c 12620
14.9%
06461e5ef0bb4941995dadba33f4b58a 12535
14.8%
5b4d18c017084084b2c8e3860625237e 12354
14.5%
b6460aef6f204ba8940329aa9f7f99cd 10914
12.8%
22da46f1ddc1406e8e6aa78cdf573c16 9298
10.9%
ebfd18e17bd1415eaa2aed61ea147c7b 8523
10.0%
fcd7f2fe3e034355bcc3fff350b5c928 6054
7.1%
6279682d7eab443facc4f8d077e5d4d2 4217
 
5.0%
b83de1e4d0124e4da7fddad09c0d6f2c 2585
 
3.0%
14564119eaff427699aadaa8c262d981 1677
 
2.0%
Other values (14) 4172
 
4.9%

Length

2025-04-12T11:50:57.278162image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
05aa66ad90564e9caf593f69bf0df44c 12620
14.9%
06461e5ef0bb4941995dadba33f4b58a 12535
14.8%
5b4d18c017084084b2c8e3860625237e 12354
14.5%
b6460aef6f204ba8940329aa9f7f99cd 10914
12.8%
22da46f1ddc1406e8e6aa78cdf573c16 9298
10.9%
ebfd18e17bd1415eaa2aed61ea147c7b 8523
10.0%
fcd7f2fe3e034355bcc3fff350b5c928 6054
7.1%
6279682d7eab443facc4f8d077e5d4d2 4217
 
5.0%
b83de1e4d0124e4da7fddad09c0d6f2c 2585
 
3.0%
14564119eaff427699aadaa8c262d981 1677
 
2.0%
Other values (14) 4172
 
4.9%

Most occurring characters

ValueCountFrequency (%)
4 239739
 
8.8%
a 225136
 
8.3%
f 206933
 
7.6%
6 202126
 
7.4%
d 194013
 
7.1%
0 184453
 
6.8%
e 177581
 
6.5%
9 170655
 
6.3%
b 161936
 
6.0%
5 158471
 
5.8%
Other values (6) 797325
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2718368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 239739
 
8.8%
a 225136
 
8.3%
f 206933
 
7.6%
6 202126
 
7.4%
d 194013
 
7.1%
0 184453
 
6.8%
e 177581
 
6.5%
9 170655
 
6.3%
b 161936
 
6.0%
5 158471
 
5.8%
Other values (6) 797325
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2718368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 239739
 
8.8%
a 225136
 
8.3%
f 206933
 
7.6%
6 202126
 
7.4%
d 194013
 
7.1%
0 184453
 
6.8%
e 177581
 
6.5%
9 170655
 
6.3%
b 161936
 
6.0%
5 158471
 
5.8%
Other values (6) 797325
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2718368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 239739
 
8.8%
a 225136
 
8.3%
f 206933
 
7.6%
6 202126
 
7.4%
d 194013
 
7.1%
0 184453
 
6.8%
e 177581
 
6.5%
9 170655
 
6.3%
b 161936
 
6.0%
5 158471
 
5.8%
Other values (6) 797325
29.3%

email
Text

Distinct78461
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
2025-04-12T11:50:57.642535image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters2718368
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72862 ?
Unique (%)85.8%

Sample

1st row4a0d5a2fe1974c1f8e8082afa8bdd72b
2nd roweb3f322048494f7ea59d63bb636772c8
3rd row93fe4d004f074298ae540784f5466926
4th rowa9a8894fee5d44da923224e46a280e9c
5th row7e7a981bfcff46aa9077f2dd2803ac57
ValueCountFrequency (%)
e33ffa74e7434baeb477b7439a0b7c4f 11
 
< 0.1%
a137146494704591a3b9c63ec4535497 9
 
< 0.1%
56fe73d8898a485da07de143a35bda4e 9
 
< 0.1%
6d9548f90748425f85eb3145cb8f72a0 8
 
< 0.1%
f740d3077d5d4081ab05c37550fb9d38 8
 
< 0.1%
8a92239cf35142bbbded084f20526b50 8
 
< 0.1%
5fd440d299ca472c9dd168dd8dec02f9 8
 
< 0.1%
a01224c98a50439a9f14a307649d8611 8
 
< 0.1%
bd467efbd63644b390ecc9888fa6f549 7
 
< 0.1%
dc1fa8efaef240df945096e8652d859a 7
 
< 0.1%
Other values (78451) 84866
99.9%
2025-04-12T11:50:58.243257image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 244785
 
9.0%
8 181055
 
6.7%
b 180507
 
6.6%
9 180430
 
6.6%
a 179733
 
6.6%
5 159884
 
5.9%
7 159596
 
5.9%
6 159477
 
5.9%
1 159311
 
5.9%
0 159305
 
5.9%
Other values (6) 954285
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2718368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 244785
 
9.0%
8 181055
 
6.7%
b 180507
 
6.6%
9 180430
 
6.6%
a 179733
 
6.6%
5 159884
 
5.9%
7 159596
 
5.9%
6 159477
 
5.9%
1 159311
 
5.9%
0 159305
 
5.9%
Other values (6) 954285
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2718368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 244785
 
9.0%
8 181055
 
6.7%
b 180507
 
6.6%
9 180430
 
6.6%
a 179733
 
6.6%
5 159884
 
5.9%
7 159596
 
5.9%
6 159477
 
5.9%
1 159311
 
5.9%
0 159305
 
5.9%
Other values (6) 954285
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2718368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 244785
 
9.0%
8 181055
 
6.7%
b 180507
 
6.6%
9 180430
 
6.6%
a 179733
 
6.6%
5 159884
 
5.9%
7 159596
 
5.9%
6 159477
 
5.9%
1 159311
 
5.9%
0 159305
 
5.9%
Other values (6) 954285
35.1%

flight_coupon_id
Real number (ℝ)

Distinct84801
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5512027.7
Minimum1000251
Maximum9999912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:50:58.459351image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1000251
5-th percentile1454788.4
Q13267038
median5515807
Q37772667
95-th percentile9556895.2
Maximum9999912
Range8999661
Interquartile range (IQR)4505629

Descriptive statistics

Standard deviation2601157.5
Coefficient of variation (CV)0.47190573
Kurtosis-1.203918
Mean5512027.7
Median Absolute Deviation (MAD)2252612
Skewness-0.002003187
Sum4.6824125 × 1011
Variance6.7660203 × 1012
MonotonicityNot monotonic
2025-04-12T11:50:58.673218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2721744 3
 
< 0.1%
2900124 3
 
< 0.1%
9811869 2
 
< 0.1%
9579095 2
 
< 0.1%
8874794 2
 
< 0.1%
8918055 2
 
< 0.1%
9990696 2
 
< 0.1%
1313193 2
 
< 0.1%
9651429 2
 
< 0.1%
9355764 2
 
< 0.1%
Other values (84791) 84927
> 99.9%
ValueCountFrequency (%)
1000251 1
< 0.1%
1000624 1
< 0.1%
1000688 1
< 0.1%
1000790 1
< 0.1%
1000843 1
< 0.1%
1000857 1
< 0.1%
1000895 1
< 0.1%
1000967 1
< 0.1%
1001110 1
< 0.1%
1001261 1
< 0.1%
ValueCountFrequency (%)
9999912 1
< 0.1%
9999846 1
< 0.1%
9999781 1
< 0.1%
9999708 1
< 0.1%
9999669 1
< 0.1%
9999629 1
< 0.1%
9999599 1
< 0.1%
9999588 1
< 0.1%
9999484 1
< 0.1%
9999457 1
< 0.1%

flight_leg_id
Real number (ℝ)

Distinct84782
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5516877.9
Minimum1000077
Maximum9999791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:50:58.867583image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1000077
5-th percentile1455697.6
Q13270782
median5525110
Q37758842
95-th percentile9552604.8
Maximum9999791
Range8999714
Interquartile range (IQR)4488060

Descriptive statistics

Standard deviation2595148.5
Coefficient of variation (CV)0.47040166
Kurtosis-1.1977262
Mean5516877.9
Median Absolute Deviation (MAD)2244565
Skewness-0.013353815
Sum4.6865326 × 1011
Variance6.734796 × 1012
MonotonicityNot monotonic
2025-04-12T11:50:59.030021image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6960253 3
 
< 0.1%
8118766 3
 
< 0.1%
2900072 2
 
< 0.1%
6517808 2
 
< 0.1%
6846697 2
 
< 0.1%
7925363 2
 
< 0.1%
4319337 2
 
< 0.1%
8863313 2
 
< 0.1%
7883876 2
 
< 0.1%
4219254 2
 
< 0.1%
Other values (84772) 84927
> 99.9%
ValueCountFrequency (%)
1000077 1
< 0.1%
1000190 1
< 0.1%
1000207 1
< 0.1%
1000357 1
< 0.1%
1000406 1
< 0.1%
1000412 1
< 0.1%
1000561 1
< 0.1%
1000615 1
< 0.1%
1000673 1
< 0.1%
1000786 1
< 0.1%
ValueCountFrequency (%)
9999791 1
< 0.1%
9999680 1
< 0.1%
9999622 1
< 0.1%
9999574 1
< 0.1%
9999565 1
< 0.1%
9999461 1
< 0.1%
9999445 1
< 0.1%
9999321 1
< 0.1%
9999209 1
< 0.1%
9998966 1
< 0.1%

is_seat
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
0
53368 
1
31581 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters84949
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 53368
62.8%
1 31581
37.2%

Length

2025-04-12T11:50:59.145208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T11:50:59.235985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 53368
62.8%
1 31581
37.2%

Most occurring characters

ValueCountFrequency (%)
0 53368
62.8%
1 31581
37.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 53368
62.8%
1 31581
37.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 53368
62.8%
1 31581
37.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 53368
62.8%
1 31581
37.2%

leg_arrival_hour
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.747931
Minimum0
Maximum23
Zeros820
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:50:59.335856image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q111
median16
Q319
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.3561062
Coefficient of variation (CV)0.36317678
Kurtosis-0.273186
Mean14.747931
Median Absolute Deviation (MAD)4
Skewness-0.59319882
Sum1252822
Variance28.687873
MonotonicityNot monotonic
2025-04-12T11:50:59.727274image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
9 8824
10.4%
13 7692
 
9.1%
18 7597
 
8.9%
19 6989
 
8.2%
17 6695
 
7.9%
21 6219
 
7.3%
12 6145
 
7.2%
22 5337
 
6.3%
20 5040
 
5.9%
14 3884
 
4.6%
Other values (14) 20527
24.2%
ValueCountFrequency (%)
0 820
 
1.0%
1 1094
 
1.3%
2 734
 
0.9%
3 316
 
0.4%
4 1034
 
1.2%
5 986
 
1.2%
6 1594
 
1.9%
7 649
 
0.8%
8 1658
 
2.0%
9 8824
10.4%
ValueCountFrequency (%)
23 1453
 
1.7%
22 5337
6.3%
21 6219
7.3%
20 5040
5.9%
19 6989
8.2%
18 7597
8.9%
17 6695
7.9%
16 3410
4.0%
15 3157
3.7%
14 3884
4.6%
Distinct52
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.1 MiB
2025-04-12T11:51:00.093740image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length19
Median length13
Mean length13.532155
Min length9

Characters and Unicode

Total characters1149543
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEurope/Warsaw
2nd rowAmerica/New_York
3rd rowEurope/Warsaw
4th rowAsia/Kolkata
5th rowEurope/Bucharest
ValueCountFrequency (%)
europe/warsaw 32045
37.7%
europe/berlin 4020
 
4.7%
america/new_york 3773
 
4.4%
europe/london 3051
 
3.6%
europe/vilnius 2312
 
2.7%
america/chicago 2157
 
2.5%
europe/zurich 2126
 
2.5%
europe/amsterdam 2087
 
2.5%
europe/paris 1950
 
2.3%
europe/bucharest 1901
 
2.2%
Other values (42) 29527
34.8%
2025-04-12T11:51:00.608248image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 137263
11.9%
e 107219
 
9.3%
a 106718
 
9.3%
o 101734
 
8.8%
u 85275
 
7.4%
/ 84949
 
7.4%
p 72624
 
6.3%
E 69896
 
6.1%
s 58511
 
5.1%
i 41130
 
3.6%
Other values (35) 284224
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1149543
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 137263
11.9%
e 107219
 
9.3%
a 106718
 
9.3%
o 101734
 
8.8%
u 85275
 
7.4%
/ 84949
 
7.4%
p 72624
 
6.3%
E 69896
 
6.1%
s 58511
 
5.1%
i 41130
 
3.6%
Other values (35) 284224
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1149543
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 137263
11.9%
e 107219
 
9.3%
a 106718
 
9.3%
o 101734
 
8.8%
u 85275
 
7.4%
/ 84949
 
7.4%
p 72624
 
6.3%
E 69896
 
6.1%
s 58511
 
5.1%
i 41130
 
3.6%
Other values (35) 284224
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1149543
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 137263
11.9%
e 107219
 
9.3%
a 106718
 
9.3%
o 101734
 
8.8%
u 85275
 
7.4%
/ 84949
 
7.4%
p 72624
 
6.3%
E 69896
 
6.1%
s 58511
 
5.1%
i 41130
 
3.6%
Other values (35) 284224
24.7%

leg_departure_hour
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.763505
Minimum0
Maximum23
Zeros18
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:51:00.808156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median14
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.0518301
Coefficient of variation (CV)0.36704531
Kurtosis-1.0384471
Mean13.763505
Median Absolute Deviation (MAD)4
Skewness-0.11599134
Sum1169196
Variance25.520987
MonotonicityNot monotonic
2025-04-12T11:51:00.964208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10 8910
 
10.5%
19 8420
 
9.9%
7 7730
 
9.1%
14 6902
 
8.1%
17 6478
 
7.6%
16 6303
 
7.4%
18 4993
 
5.9%
22 4285
 
5.0%
12 3996
 
4.7%
8 3732
 
4.4%
Other values (14) 23200
27.3%
ValueCountFrequency (%)
0 18
 
< 0.1%
1 251
 
0.3%
2 55
 
0.1%
3 321
 
0.4%
4 273
 
0.3%
5 2647
 
3.1%
6 1955
 
2.3%
7 7730
9.1%
8 3732
4.4%
9 2505
 
2.9%
ValueCountFrequency (%)
23 1256
 
1.5%
22 4285
5.0%
21 1678
 
2.0%
20 3175
 
3.7%
19 8420
9.9%
18 4993
5.9%
17 6478
7.6%
16 6303
7.4%
15 3448
4.1%
14 6902
8.1%

leg_departure_month
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4200873
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:51:01.125132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median9
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.3561857
Coefficient of variation (CV)0.39859274
Kurtosis0.12347817
Mean8.4200873
Median Absolute Deviation (MAD)1
Skewness-1.2618251
Sum715278
Variance11.263982
MonotonicityNot monotonic
2025-04-12T11:51:01.274331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
9 24732
29.1%
10 23219
27.3%
11 10909
12.8%
12 8322
 
9.8%
2 7470
 
8.8%
1 5042
 
5.9%
3 4277
 
5.0%
8 978
 
1.2%
ValueCountFrequency (%)
1 5042
 
5.9%
2 7470
 
8.8%
3 4277
 
5.0%
8 978
 
1.2%
9 24732
29.1%
10 23219
27.3%
11 10909
12.8%
12 8322
 
9.8%
ValueCountFrequency (%)
12 8322
 
9.8%
11 10909
12.8%
10 23219
27.3%
9 24732
29.1%
8 978
 
1.2%
3 4277
 
5.0%
2 7470
 
8.8%
1 5042
 
5.9%
Distinct52
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.1 MiB
2025-04-12T11:51:01.602092image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length19
Median length13
Mean length13.507281
Min length9

Characters and Unicode

Total characters1147430
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsia/Tbilisi
2nd rowEurope/Warsaw
3rd rowEurope/Belgrade
4th rowEurope/Warsaw
5th rowEurope/Oslo
ValueCountFrequency (%)
europe/warsaw 33380
39.3%
europe/berlin 3828
 
4.5%
america/new_york 3192
 
3.8%
europe/london 2817
 
3.3%
europe/vilnius 2583
 
3.0%
europe/bucharest 2037
 
2.4%
europe/zurich 1849
 
2.2%
america/chicago 1829
 
2.2%
europe/paris 1787
 
2.1%
europe/amsterdam 1747
 
2.1%
Other values (42) 29900
35.2%
2025-04-12T11:51:02.132237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 137280
12.0%
a 108478
 
9.5%
e 106334
 
9.3%
o 100964
 
8.8%
u 86856
 
7.6%
/ 84949
 
7.4%
p 74077
 
6.5%
E 71354
 
6.2%
s 59466
 
5.2%
i 40236
 
3.5%
Other values (35) 277436
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1147430
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 137280
12.0%
a 108478
 
9.5%
e 106334
 
9.3%
o 100964
 
8.8%
u 86856
 
7.6%
/ 84949
 
7.4%
p 74077
 
6.5%
E 71354
 
6.2%
s 59466
 
5.2%
i 40236
 
3.5%
Other values (35) 277436
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1147430
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 137280
12.0%
a 108478
 
9.5%
e 106334
 
9.3%
o 100964
 
8.8%
u 86856
 
7.6%
/ 84949
 
7.4%
p 74077
 
6.5%
E 71354
 
6.2%
s 59466
 
5.2%
i 40236
 
3.5%
Other values (35) 277436
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1147430
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 137280
12.0%
a 108478
 
9.5%
e 106334
 
9.3%
o 100964
 
8.8%
u 86856
 
7.6%
/ 84949
 
7.4%
p 74077
 
6.5%
E 71354
 
6.2%
s 59466
 
5.2%
i 40236
 
3.5%
Other values (35) 277436
24.2%
Distinct87
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-04-12T11:51:02.544914image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters254847
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWAW
2nd rowEWR
3rd rowSZZ
4th rowBOM
5th rowOTP
ValueCountFrequency (%)
waw 27264
32.1%
lhr 2662
 
3.1%
vno 2312
 
2.7%
ord 2157
 
2.5%
ams 2087
 
2.5%
jfk 2085
 
2.5%
yyz 1861
 
2.2%
krk 1820
 
2.1%
otp 1536
 
1.8%
prg 1451
 
1.7%
Other values (77) 39714
46.8%
2025-04-12T11:51:03.071928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 56496
22.2%
A 37465
14.7%
R 18683
 
7.3%
O 11409
 
4.5%
L 10681
 
4.2%
D 8944
 
3.5%
T 8303
 
3.3%
S 8116
 
3.2%
N 7800
 
3.1%
C 7744
 
3.0%
Other values (16) 79206
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 254847
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 56496
22.2%
A 37465
14.7%
R 18683
 
7.3%
O 11409
 
4.5%
L 10681
 
4.2%
D 8944
 
3.5%
T 8303
 
3.3%
S 8116
 
3.2%
N 7800
 
3.1%
C 7744
 
3.0%
Other values (16) 79206
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 254847
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 56496
22.2%
A 37465
14.7%
R 18683
 
7.3%
O 11409
 
4.5%
L 10681
 
4.2%
D 8944
 
3.5%
T 8303
 
3.3%
S 8116
 
3.2%
N 7800
 
3.1%
C 7744
 
3.0%
Other values (16) 79206
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 254847
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 56496
22.2%
A 37465
14.7%
R 18683
 
7.3%
O 11409
 
4.5%
L 10681
 
4.2%
D 8944
 
3.5%
T 8303
 
3.3%
S 8116
 
3.2%
N 7800
 
3.1%
C 7744
 
3.0%
Other values (16) 79206
31.1%

leg_destination_country_code
Categorical

High correlation  Missing 

Distinct45
Distinct (%)0.2%
Missing65106
Missing (%)76.6%
Memory size4.4 MiB
PL
8932 
US
2281 
DE
 
744
IT
 
679
CH
 
608
Other values (40)
6599 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters39686
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowPL
3rd rowPL
4th rowDK
5th rowCZ

Common Values

ValueCountFrequency (%)
PL 8932
 
10.5%
US 2281
 
2.7%
DE 744
 
0.9%
IT 679
 
0.8%
CH 608
 
0.7%
GB 561
 
0.7%
NL 524
 
0.6%
FR 511
 
0.6%
ES 435
 
0.5%
TR 434
 
0.5%
Other values (35) 4134
 
4.9%
(Missing) 65106
76.6%

Length

2025-04-12T11:51:03.258139image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pl 8932
45.0%
us 2281
 
11.5%
de 744
 
3.7%
it 679
 
3.4%
ch 608
 
3.1%
gb 561
 
2.8%
nl 524
 
2.6%
fr 511
 
2.6%
es 435
 
2.2%
tr 434
 
2.2%
Other values (35) 4134
20.8%

Most occurring characters

ValueCountFrequency (%)
L 9783
24.7%
P 9216
23.2%
S 3041
 
7.7%
U 2543
 
6.4%
E 2253
 
5.7%
R 1639
 
4.1%
T 1344
 
3.4%
G 1177
 
3.0%
C 1154
 
2.9%
B 1007
 
2.5%
Other values (13) 6529
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39686
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 9783
24.7%
P 9216
23.2%
S 3041
 
7.7%
U 2543
 
6.4%
E 2253
 
5.7%
R 1639
 
4.1%
T 1344
 
3.4%
G 1177
 
3.0%
C 1154
 
2.9%
B 1007
 
2.5%
Other values (13) 6529
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39686
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 9783
24.7%
P 9216
23.2%
S 3041
 
7.7%
U 2543
 
6.4%
E 2253
 
5.7%
R 1639
 
4.1%
T 1344
 
3.4%
G 1177
 
3.0%
C 1154
 
2.9%
B 1007
 
2.5%
Other values (13) 6529
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39686
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 9783
24.7%
P 9216
23.2%
S 3041
 
7.7%
U 2543
 
6.4%
E 2253
 
5.7%
R 1639
 
4.1%
T 1344
 
3.4%
G 1177
 
3.0%
C 1154
 
2.9%
B 1007
 
2.5%
Other values (13) 6529
16.5%

leg_duration_h
Real number (ℝ)

Zeros 

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8904755
Minimum0
Maximum45
Zeros3115
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:51:03.433404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile18
Maximum45
Range45
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.4787245
Coefficient of variation (CV)1.1202846
Kurtosis4.4896323
Mean4.8904755
Median Absolute Deviation (MAD)2
Skewness2.0912358
Sum415441
Variance30.016422
MonotonicityNot monotonic
2025-04-12T11:51:03.631038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1 17585
20.7%
2 16889
19.9%
3 13704
16.1%
4 7083
8.3%
5 5352
 
6.3%
6 3564
 
4.2%
0 3115
 
3.7%
14 2866
 
3.4%
7 2369
 
2.8%
16 1614
 
1.9%
Other values (35) 10808
12.7%
ValueCountFrequency (%)
0 3115
 
3.7%
1 17585
20.7%
2 16889
19.9%
3 13704
16.1%
4 7083
8.3%
5 5352
 
6.3%
6 3564
 
4.2%
7 2369
 
2.8%
8 1377
 
1.6%
9 1251
 
1.5%
ValueCountFrequency (%)
45 1
 
< 0.1%
43 1
 
< 0.1%
42 1
 
< 0.1%
41 1
 
< 0.1%
40 1
 
< 0.1%
39 6
 
< 0.1%
38 4
 
< 0.1%
37 3
 
< 0.1%
36 4
 
< 0.1%
35 36
< 0.1%

leg_first_leg_flg
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
1
53664 
0
31285 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters84949
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 53664
63.2%
0 31285
36.8%

Length

2025-04-12T11:51:03.802214image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T11:51:03.919919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 53664
63.2%
0 31285
36.8%

Most occurring characters

ValueCountFrequency (%)
1 53664
63.2%
0 31285
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 53664
63.2%
0 31285
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 53664
63.2%
0 31285
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 53664
63.2%
0 31285
36.8%

leg_hours_to_departure
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.753111
Minimum61
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:51:04.035694image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile65
Q171
median71
Q371
95-th percentile73
Maximum80
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.2920686
Coefficient of variation (CV)0.032395305
Kurtosis5.0003351
Mean70.753111
Median Absolute Deviation (MAD)0
Skewness-1.1170164
Sum6010406
Variance5.2535785
MonotonicityNot monotonic
2025-04-12T11:51:04.158141image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
71 55888
65.8%
72 10344
 
12.2%
70 5579
 
6.6%
65 4543
 
5.3%
64 1687
 
2.0%
74 1571
 
1.8%
73 1302
 
1.5%
78 1100
 
1.3%
75 935
 
1.1%
62 593
 
0.7%
Other values (8) 1407
 
1.7%
ValueCountFrequency (%)
61 103
 
0.1%
62 593
 
0.7%
63 79
 
0.1%
64 1687
 
2.0%
65 4543
 
5.3%
66 329
 
0.4%
69 337
 
0.4%
70 5579
 
6.6%
71 55888
65.8%
72 10344
 
12.2%
ValueCountFrequency (%)
80 21
 
< 0.1%
79 314
 
0.4%
78 1100
 
1.3%
77 69
 
0.1%
76 155
 
0.2%
75 935
 
1.1%
74 1571
 
1.8%
73 1302
 
1.5%
72 10344
 
12.2%
71 55888
65.8%

leg_last_leg_flg
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
1
54020 
0
30929 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters84949
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 54020
63.6%
0 30929
36.4%

Length

2025-04-12T11:51:04.264174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T11:51:04.362305image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 54020
63.6%
0 30929
36.4%

Most occurring characters

ValueCountFrequency (%)
1 54020
63.6%
0 30929
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 54020
63.6%
0 30929
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 54020
63.6%
0 30929
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 54020
63.6%
0 30929
36.4%

leg_number
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
1
57564 
2
27305 
3
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters84949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 57564
67.8%
2 27305
32.1%
3 80
 
0.1%

Length

2025-04-12T11:51:04.488868image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T11:51:04.616599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 57564
67.8%
2 27305
32.1%
3 80
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 57564
67.8%
2 27305
32.1%
3 80
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 57564
67.8%
2 27305
32.1%
3 80
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 57564
67.8%
2 27305
32.1%
3 80
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 57564
67.8%
2 27305
32.1%
3 80
 
0.1%
Distinct87
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-04-12T11:51:04.941757image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters254847
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTBS
2nd rowWAW
3rd rowBEG
4th rowWAW
5th rowOSL
ValueCountFrequency (%)
waw 28484
33.5%
vno 2583
 
3.0%
lhr 2472
 
2.9%
krk 1873
 
2.2%
ord 1829
 
2.2%
ams 1747
 
2.1%
jfk 1707
 
2.0%
otp 1599
 
1.9%
tll 1579
 
1.9%
yyz 1565
 
1.8%
Other values (77) 39511
46.5%
2025-04-12T11:51:05.445735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 58868
23.1%
A 38077
14.9%
R 18022
 
7.1%
O 11721
 
4.6%
L 10867
 
4.3%
T 8828
 
3.5%
D 8499
 
3.3%
S 8454
 
3.3%
N 7836
 
3.1%
C 7217
 
2.8%
Other values (16) 76458
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 254847
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 58868
23.1%
A 38077
14.9%
R 18022
 
7.1%
O 11721
 
4.6%
L 10867
 
4.3%
T 8828
 
3.5%
D 8499
 
3.3%
S 8454
 
3.3%
N 7836
 
3.1%
C 7217
 
2.8%
Other values (16) 76458
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 254847
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 58868
23.1%
A 38077
14.9%
R 18022
 
7.1%
O 11721
 
4.6%
L 10867
 
4.3%
T 8828
 
3.5%
D 8499
 
3.3%
S 8454
 
3.3%
N 7836
 
3.1%
C 7217
 
2.8%
Other values (16) 76458
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 254847
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 58868
23.1%
A 38077
14.9%
R 18022
 
7.1%
O 11721
 
4.6%
L 10867
 
4.3%
T 8828
 
3.5%
D 8499
 
3.3%
S 8454
 
3.3%
N 7836
 
3.1%
C 7217
 
2.8%
Other values (16) 76458
30.0%

leg_origin_country_code
Categorical

High correlation  Missing 

Distinct44
Distinct (%)0.2%
Missing65079
Missing (%)76.6%
Memory size4.4 MiB
PL
10611 
US
1956 
DE
 
673
IT
 
519
GB
 
466
Other values (39)
5645 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters39740
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowES
2nd rowKR
3rd rowPL
4th rowPL
5th rowCH

Common Values

ValueCountFrequency (%)
PL 10611
 
12.5%
US 1956
 
2.3%
DE 673
 
0.8%
IT 519
 
0.6%
GB 466
 
0.5%
CH 460
 
0.5%
FR 443
 
0.5%
ES 388
 
0.5%
NL 385
 
0.5%
TR 345
 
0.4%
Other values (34) 3624
 
4.3%
(Missing) 65079
76.6%

Length

2025-04-12T11:51:05.633660image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pl 10611
53.4%
us 1956
 
9.8%
de 673
 
3.4%
it 519
 
2.6%
gb 466
 
2.3%
ch 460
 
2.3%
fr 443
 
2.2%
es 388
 
2.0%
nl 385
 
1.9%
tr 345
 
1.7%
Other values (34) 3624
 
18.2%

Most occurring characters

ValueCountFrequency (%)
L 11259
28.3%
P 10793
27.2%
S 2662
 
6.7%
U 2191
 
5.5%
E 2003
 
5.0%
R 1442
 
3.6%
T 1058
 
2.7%
G 994
 
2.5%
C 885
 
2.2%
D 874
 
2.2%
Other values (13) 5579
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 11259
28.3%
P 10793
27.2%
S 2662
 
6.7%
U 2191
 
5.5%
E 2003
 
5.0%
R 1442
 
3.6%
T 1058
 
2.7%
G 994
 
2.5%
C 885
 
2.2%
D 874
 
2.2%
Other values (13) 5579
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 11259
28.3%
P 10793
27.2%
S 2662
 
6.7%
U 2191
 
5.5%
E 2003
 
5.0%
R 1442
 
3.6%
T 1058
 
2.7%
G 994
 
2.5%
C 885
 
2.2%
D 874
 
2.2%
Other values (13) 5579
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 11259
28.3%
P 10793
27.2%
S 2662
 
6.7%
U 2191
 
5.5%
E 2003
 
5.0%
R 1442
 
3.6%
T 1058
 
2.7%
G 994
 
2.5%
C 885
 
2.2%
D 874
 
2.2%
Other values (13) 5579
14.0%

leg_stopover_time_h
Real number (ℝ)

High correlation 

Distinct2031
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6240.4644
Minimum-9999
Maximum8771
Zeros0
Zeros (%)0.0%
Negative54020
Negative (%)63.6%
Memory size663.8 KiB
2025-04-12T11:51:05.809774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile-9999
Q1-9999
median-9999
Q399
95-th percentile492
Maximum8771
Range18770
Interquartile range (IQR)10098

Descriptive statistics

Standard deviation4980.8746
Coefficient of variation (CV)-0.79815767
Kurtosis-1.6120766
Mean-6240.4644
Median Absolute Deviation (MAD)0
Skewness0.58604442
Sum-5.3012121 × 108
Variance24809111
MonotonicityNot monotonic
2025-04-12T11:51:05.980505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9999 54020
63.6%
75 479
 
0.6%
84 393
 
0.5%
74 392
 
0.5%
99 351
 
0.4%
79 287
 
0.3%
51 285
 
0.3%
76 285
 
0.3%
77 280
 
0.3%
108 260
 
0.3%
Other values (2021) 27917
32.9%
ValueCountFrequency (%)
-9999 54020
63.6%
3 1
 
< 0.1%
6 1
 
< 0.1%
7 5
 
< 0.1%
8 5
 
< 0.1%
9 7
 
< 0.1%
10 17
 
< 0.1%
11 8
 
< 0.1%
12 25
 
< 0.1%
13 13
 
< 0.1%
ValueCountFrequency (%)
8771 1
< 0.1%
8616 1
< 0.1%
8602 1
< 0.1%
8555 1
< 0.1%
8412 1
< 0.1%
8411 1
< 0.1%
8290 1
< 0.1%
8214 1
< 0.1%
8127 1
< 0.1%
8117 1
< 0.1%

pnr
Text

Distinct81730
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
2025-04-12T11:51:06.403984image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters2718368
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78610 ?
Unique (%)92.5%

Sample

1st row40faefe514be47b38cea3be065583d7f
2nd row7fafefddde544aba95c69552cfbadd51
3rd row6240c081718e4439a3174ac38a2c9c45
4th row231c99e3d0644366827e91276296e3cc
5th row4854301527cf48f7a32c9625a154874b
ValueCountFrequency (%)
7bbdfd259c424672aaec55b4ed21012b 11
 
< 0.1%
3fd51e95bf884d2d9b0950c2bc674127 7
 
< 0.1%
9846d8372bc34bfc8c49f0ea00a4a2c5 6
 
< 0.1%
8a67aa5b47a842a9b56fc29e30c65a41 5
 
< 0.1%
35789d3d925a48ac90178695077c1a20 5
 
< 0.1%
e897a35961fd456b8d8ba989a418e40f 5
 
< 0.1%
52afcd9f9c9949fcb40987f28572e65c 5
 
< 0.1%
8f392122ed114131a7f700e24be0bc3b 5
 
< 0.1%
fb0fe9571e1a492ab5c081a047286fbe 4
 
< 0.1%
991a40de96464afe8d3d7eef7b76b403 4
 
< 0.1%
Other values (81720) 84892
99.9%
2025-04-12T11:51:06.975692image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 244297
 
9.0%
9 180501
 
6.6%
a 180332
 
6.6%
b 180247
 
6.6%
8 179880
 
6.6%
e 159801
 
5.9%
7 159637
 
5.9%
c 159581
 
5.9%
f 159418
 
5.9%
1 159357
 
5.9%
Other values (6) 955317
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2718368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 244297
 
9.0%
9 180501
 
6.6%
a 180332
 
6.6%
b 180247
 
6.6%
8 179880
 
6.6%
e 159801
 
5.9%
7 159637
 
5.9%
c 159581
 
5.9%
f 159418
 
5.9%
1 159357
 
5.9%
Other values (6) 955317
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2718368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 244297
 
9.0%
9 180501
 
6.6%
a 180332
 
6.6%
b 180247
 
6.6%
8 179880
 
6.6%
e 159801
 
5.9%
7 159637
 
5.9%
c 159581
 
5.9%
f 159418
 
5.9%
1 159357
 
5.9%
Other values (6) 955317
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2718368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 244297
 
9.0%
9 180501
 
6.6%
a 180332
 
6.6%
b 180247
 
6.6%
8 179880
 
6.6%
e 159801
 
5.9%
7 159637
 
5.9%
c 159581
 
5.9%
f 159418
 
5.9%
1 159357
 
5.9%
Other values (6) 955317
35.1%

preffered_language
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
en_gb
65438 
pl_pl
19511 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters424745
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowen_gb
2nd rowen_gb
3rd rowen_gb
4th rowen_gb
5th rowen_gb

Common Values

ValueCountFrequency (%)
en_gb 65438
77.0%
pl_pl 19511
 
23.0%

Length

2025-04-12T11:51:07.188686image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T11:51:07.324872image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
en_gb 65438
77.0%
pl_pl 19511
 
23.0%

Most occurring characters

ValueCountFrequency (%)
_ 84949
20.0%
e 65438
15.4%
n 65438
15.4%
g 65438
15.4%
b 65438
15.4%
p 39022
9.2%
l 39022
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 424745
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 84949
20.0%
e 65438
15.4%
n 65438
15.4%
g 65438
15.4%
b 65438
15.4%
p 39022
9.2%
l 39022
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 424745
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 84949
20.0%
e 65438
15.4%
n 65438
15.4%
g 65438
15.4%
b 65438
15.4%
p 39022
9.2%
l 39022
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 424745
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 84949
20.0%
e 65438
15.4%
n 65438
15.4%
g 65438
15.4%
b 65438
15.4%
p 39022
9.2%
l 39022
9.2%
Distinct4133
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size663.8 KiB
Minimum2013-09-14 19:04:16.417000+00:00
Maximum2014-03-31 04:12:26.013000+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-12T11:51:07.494746image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:51:07.655920image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct4133
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
2025-04-12T11:51:07.947124image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters2718368
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique169 ?
Unique (%)0.2%

Sample

1st rowee37548ce2a74b99b41d67f4d2a138fd
2nd rowf083457969dd46d2ada761198096ecc4
3rd row9df9a894f4374dfba0d8811dd99c1b1e
4th row89c95fa040db42d4a1982c7901128585
5th rowd44b65a240374e61867039b37c98f9a0
ValueCountFrequency (%)
39a540adca3c4aec9af5f90d5b0bfc1d 146
 
0.2%
000a88383de8468db61e4d1a18c827ef 138
 
0.2%
7887589e65a344a380c87efcc682456c 117
 
0.1%
d5a8015576df42048f289bc9d411b80e 115
 
0.1%
699208e0cce04b66835be483ff63389c 114
 
0.1%
c39075c74f2345eea9d09d21ca931bb4 112
 
0.1%
44148fae00a44a1f9edbdb727b8dfdaf 110
 
0.1%
0770b83e2dd94da685bf6e62e78388db 109
 
0.1%
cbd63f2c0ddb4a55bb7dfd6302a91a10 108
 
0.1%
4ceaab8a97274997813ba0135dcf7ec0 108
 
0.1%
Other values (4123) 83772
98.6%
2025-04-12T11:51:08.451040image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 242452
 
8.9%
8 183508
 
6.8%
a 182636
 
6.7%
9 181797
 
6.7%
b 180619
 
6.6%
1 164127
 
6.0%
2 163027
 
6.0%
d 159905
 
5.9%
3 159749
 
5.9%
5 158988
 
5.8%
Other values (6) 941560
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2718368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 242452
 
8.9%
8 183508
 
6.8%
a 182636
 
6.7%
9 181797
 
6.7%
b 180619
 
6.6%
1 164127
 
6.0%
2 163027
 
6.0%
d 159905
 
5.9%
3 159749
 
5.9%
5 158988
 
5.8%
Other values (6) 941560
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2718368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 242452
 
8.9%
8 183508
 
6.8%
a 182636
 
6.7%
9 181797
 
6.7%
b 180619
 
6.6%
1 164127
 
6.0%
2 163027
 
6.0%
d 159905
 
5.9%
3 159749
 
5.9%
5 158988
 
5.8%
Other values (6) 941560
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2718368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 242452
 
8.9%
8 183508
 
6.8%
a 182636
 
6.7%
9 181797
 
6.7%
b 180619
 
6.6%
1 164127
 
6.0%
2 163027
 
6.0%
d 159905
 
5.9%
3 159749
 
5.9%
5 158988
 
5.8%
Other values (6) 941560
34.6%

clicked
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
0
57089 
1
27860 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters84949
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 57089
67.2%
1 27860
32.8%

Length

2025-04-12T11:51:08.662706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T11:51:08.875108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 57089
67.2%
1 27860
32.8%

Most occurring characters

ValueCountFrequency (%)
0 57089
67.2%
1 27860
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 57089
67.2%
1 27860
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 57089
67.2%
1 27860
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 57089
67.2%
1 27860
32.8%

graphic_design
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
B
34114 
A
33890 
C
16945 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters84949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowB
3rd rowC
4th rowA
5th rowB

Common Values

ValueCountFrequency (%)
B 34114
40.2%
A 33890
39.9%
C 16945
19.9%

Length

2025-04-12T11:51:09.017894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T11:51:09.169975image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
b 34114
40.2%
a 33890
39.9%
c 16945
19.9%

Most occurring characters

ValueCountFrequency (%)
B 34114
40.2%
A 33890
39.9%
C 16945
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 34114
40.2%
A 33890
39.9%
C 16945
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 34114
40.2%
A 33890
39.9%
C 16945
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 34114
40.2%
A 33890
39.9%
C 16945
19.9%

top_1_section
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
3
42453 
4
17122 
1
16956 
6
8418 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters84949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 42453
50.0%
4 17122
20.2%
1 16956
 
20.0%
6 8418
 
9.9%

Length

2025-04-12T11:51:09.326487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T11:51:09.469400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 42453
50.0%
4 17122
20.2%
1 16956
 
20.0%
6 8418
 
9.9%

Most occurring characters

ValueCountFrequency (%)
3 42453
50.0%
4 17122
20.2%
1 16956
 
20.0%
6 8418
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 42453
50.0%
4 17122
20.2%
1 16956
 
20.0%
6 8418
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 42453
50.0%
4 17122
20.2%
1 16956
 
20.0%
6 8418
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 42453
50.0%
4 17122
20.2%
1 16956
 
20.0%
6 8418
 
9.9%

top_2_section
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4024532
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:51:09.574886image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.4980915
Coefficient of variation (CV)0.44029744
Kurtosis-1.0131755
Mean3.4024532
Median Absolute Deviation (MAD)1
Skewness0.19027042
Sum289035
Variance2.244278
MonotonicityNot monotonic
2025-04-12T11:51:09.681141image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 25436
29.9%
5 17048
20.1%
2 16947
19.9%
6 8527
 
10.0%
1 8511
 
10.0%
4 8480
 
10.0%
ValueCountFrequency (%)
1 8511
 
10.0%
2 16947
19.9%
3 25436
29.9%
4 8480
 
10.0%
5 17048
20.1%
6 8527
 
10.0%
ValueCountFrequency (%)
6 8527
 
10.0%
5 17048
20.1%
4 8480
 
10.0%
3 25436
29.9%
2 16947
19.9%
1 8511
 
10.0%

top_3_section
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5058211
Minimum0
Maximum5
Zeros16945
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size663.8 KiB
2025-04-12T11:51:09.776910image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7466911
Coefficient of variation (CV)0.69705336
Kurtosis-1.2086607
Mean2.5058211
Median Absolute Deviation (MAD)1
Skewness-0.0051259014
Sum212867
Variance3.0509296
MonotonicityNot monotonic
2025-04-12T11:51:09.886425image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 17066
20.1%
3 17060
20.1%
0 16945
19.9%
2 16943
19.9%
4 8512
10.0%
1 8423
9.9%
ValueCountFrequency (%)
0 16945
19.9%
1 8423
9.9%
2 16943
19.9%
3 17060
20.1%
4 8512
10.0%
5 17066
20.1%
ValueCountFrequency (%)
5 17066
20.1%
4 8512
10.0%
3 17060
20.1%
2 16943
19.9%
1 8423
9.9%
0 16945
19.9%

Interactions

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2025-04-12T11:49:34.375436image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2025-04-12T11:50:39.566247image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:42.778408image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:49:37.459874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:49:40.555233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:49:43.599114image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:49:47.058597image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:49:49.960575image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:49:53.178708image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:49:56.642186image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:49:59.727200image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:02.942120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:06.242258image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:09.732746image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:12.958203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:16.383746image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:19.659348image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:22.898845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:26.192904image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:29.660097image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:32.910980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:36.208273image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:39.741371image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:42.931897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:49:37.608562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:49:40.700906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:49:43.743923image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:49:47.195786image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:49:50.116029image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:49:53.325501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:49:56.793474image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:49:59.874804image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:03.091529image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:06.680797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:09.874825image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:13.108052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:16.498919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:19.791335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:23.042701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:26.367289image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:29.815335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:33.063664image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:36.378256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-12T11:50:39.878534image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2025-04-12T11:51:10.020060image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
booking_adult_countbooking_child_countbooking_idbooking_infant_countbooking_leg_countbooking_original_currencybooking_pax_countbooking_payment_methodbooking_reservation_monthbooking_sales_channelbooking_segments_countbooking_trip_typebooking_window_wclickedcoupon_cabin_classcoupon_marketing_flight_numbercoupon_milescoupon_numbercoupon_rangecoupon_rbd_codeflight_coupon_idflight_leg_idgraphic_designidis_seatleg_arrival_hourleg_departure_hourleg_departure_monthleg_destination_country_codeleg_duration_hleg_first_leg_flgleg_hours_to_departureleg_last_leg_flgleg_numberleg_origin_country_codeleg_stopover_time_hpreffered_languagetop_1_sectiontop_2_sectiontop_3_section
booking_adult_count1.0000.168-0.0040.1170.0140.0000.9560.072-0.0740.0930.0490.0230.2310.0010.000-0.0070.0760.0290.0030.356-0.001-0.0050.005-0.0350.052-0.017-0.013-0.0080.0210.0440.0110.0040.0110.0060.0000.0330.0230.0000.0030.003
booking_child_count0.1681.000-0.0050.0770.0190.0150.3750.015-0.0100.0260.0210.0290.0730.0240.004-0.0130.0490.0230.0080.010-0.0030.0050.0030.0280.0080.010-0.0100.0040.0310.0220.018-0.0010.0130.0210.0280.0210.0270.0040.0030.007
booking_id-0.004-0.0051.0000.0060.0030.000-0.0040.0000.0000.003-0.0000.000-0.0010.0070.003-0.000-0.002-0.0030.0000.001-0.0050.0040.000-0.0060.000-0.0030.0020.0050.0030.0040.000-0.0010.0060.0020.003-0.0020.0020.004-0.000-0.002
booking_infant_count0.1170.0770.0061.0000.0420.0000.1210.0060.0130.0270.0000.0070.0090.0280.0000.0110.0050.0050.0000.0330.0040.0000.0000.0090.0150.0090.0110.0050.0310.0120.0110.0060.0000.0200.0090.0000.0030.0000.0010.004
booking_leg_count0.0140.0190.0030.0421.0000.1110.0140.0330.0540.0690.4440.6970.0620.0100.0310.0330.0710.1710.0680.1300.0050.0050.0040.0240.0500.0340.0480.0210.0710.0740.4640.0730.4590.4820.1340.2650.1290.0060.0040.005
booking_original_currency0.0000.0150.0000.0000.1111.0000.0070.2450.0500.2830.1270.1490.0430.0680.1420.1470.3490.0860.3220.0550.0090.0050.0100.0450.1620.1270.1820.0540.1790.1680.0790.2650.0770.0750.2310.0680.5920.0000.0050.010
booking_pax_count0.9560.375-0.0040.1210.0140.0071.0000.070-0.0710.0900.0480.0220.2320.0040.000-0.0100.0820.0320.0050.338-0.002-0.0030.007-0.0300.050-0.014-0.015-0.0060.0220.0450.0090.0040.0110.0080.0120.0350.0220.0000.0050.004
booking_payment_method0.0720.0150.0000.0060.0330.2450.0701.0000.0620.1370.0280.0370.0640.0190.0230.0260.0660.0310.0650.1250.0000.0000.0050.0210.0530.0410.0490.0160.1140.0580.0220.0600.0200.0340.1160.0150.0870.0000.0030.000
booking_reservation_month-0.074-0.0100.0000.0130.0540.050-0.0710.0621.0000.041-0.0220.084-0.2460.0740.0420.014-0.106-0.0470.1080.0590.0010.0000.0000.1530.0220.011-0.0230.5280.074-0.0510.0440.0400.0430.0910.081-0.0510.0820.000-0.0030.001
booking_sales_channel0.0930.0260.0030.0270.0690.2830.0900.1370.0411.0000.0460.0950.0490.0290.0660.0200.0600.0260.0660.3000.0030.0060.0000.0200.0870.0210.0390.0180.1000.0450.0550.0430.0510.0430.0870.0300.3480.0000.0010.000
booking_segments_count0.0490.021-0.0000.0000.4440.1270.0480.028-0.0220.0461.0000.6300.0550.0190.023-0.020-0.0190.4980.0620.0670.0010.0020.0050.0470.095-0.014-0.060-0.0030.0780.4430.3500.0900.3440.2450.1140.2980.1710.0030.002-0.003
booking_trip_type0.0230.0290.0000.0070.6970.1490.0220.0370.0840.0950.6301.0000.1000.0150.0390.0390.0950.2200.0890.1740.0040.0040.0000.0690.0420.0480.0610.0640.0980.0820.4460.0810.4410.2930.1530.3120.1440.0030.0030.000
booking_window_w0.2310.073-0.0010.0090.0620.0430.2320.064-0.2460.0490.0550.1001.0000.0590.073-0.0220.1630.0260.1140.1030.0010.0050.000-0.1050.035-0.0160.0310.0180.0760.1010.050-0.0540.0510.0490.0640.0800.1120.0040.000-0.002
clicked0.0010.0240.0070.0280.0100.0680.0040.0190.0740.0290.0190.0150.0591.0000.0350.0210.0770.0120.0760.0360.0000.0000.0000.0770.0000.0340.0490.0700.0640.0660.0000.0660.0060.0130.0940.0060.0040.0000.0060.008
coupon_cabin_class0.0000.0040.0030.0000.0310.1420.0000.0230.0420.0660.0230.0390.0730.0351.0000.0640.2370.0160.2330.9980.0050.0010.0000.0250.1490.0600.0880.0230.1620.1300.0160.1350.0120.0260.1530.0300.0120.0060.0000.000
coupon_marketing_flight_number-0.007-0.013-0.0000.0110.0330.147-0.0100.0260.0140.020-0.0200.039-0.0220.0210.0641.000-0.175-0.0430.1490.035-0.000-0.0030.0060.0200.026-0.0320.0630.0010.423-0.0840.025-0.0140.0230.0430.396-0.0300.0260.008-0.0030.008
coupon_miles0.0760.049-0.0020.0050.0710.3490.0820.066-0.1060.060-0.0190.0950.1630.0770.237-0.1751.0000.0550.5860.141-0.0040.0010.008-0.0640.135-0.0290.154-0.0350.5570.4700.058-0.1880.0550.0800.4950.1260.0450.0050.003-0.005
coupon_number0.0290.023-0.0030.0050.1710.0860.0320.031-0.0470.0260.4980.2200.0260.0120.016-0.0430.0551.0000.0510.0360.0000.0060.006-0.0010.0490.1070.066-0.0100.2230.2450.1370.0900.1260.5660.2630.1510.1080.0030.003-0.007
coupon_range0.0030.0080.0000.0000.0680.3220.0050.0650.1080.0660.0620.0890.1140.0760.2330.1490.5860.0511.0000.1530.0000.0000.0040.0600.0740.1420.2310.0580.4460.3300.0570.3260.0530.0750.3980.0900.0000.0030.0020.002
coupon_rbd_code0.3560.0100.0010.0330.1300.0550.3380.1250.0590.3000.0670.1740.1030.0360.9980.0350.1410.0360.1531.0000.0000.0090.0000.0560.2020.0410.0500.0720.0680.0750.1050.0840.0970.0780.0740.0500.1350.0000.0000.000
flight_coupon_id-0.001-0.003-0.0050.0040.0050.009-0.0020.0000.0010.0030.0010.0040.0010.0000.005-0.000-0.0040.0000.0000.0001.000-0.0050.000-0.0040.009-0.004-0.005-0.0010.0000.0040.013-0.0000.0110.0000.008-0.0000.0050.004-0.0010.003
flight_leg_id-0.0050.0050.0040.0000.0050.005-0.0030.0000.0000.0060.0020.0040.0050.0000.001-0.0030.0010.0060.0000.009-0.0051.0000.002-0.0010.000-0.004-0.001-0.0030.0030.0080.003-0.0040.0000.0000.010-0.0010.0000.000-0.005-0.002
graphic_design0.0050.0030.0000.0000.0040.0100.0070.0050.0000.0000.0050.0000.0000.0000.0000.0060.0080.0060.0040.0000.0000.0021.0000.0050.0000.0070.0000.0060.0090.0000.0050.0040.0010.0050.0000.0000.0000.7070.8170.935
id-0.0350.028-0.0060.0090.0240.045-0.0300.0210.1530.0200.0470.069-0.1050.0770.0250.020-0.064-0.0010.0600.056-0.004-0.0010.0051.0000.0260.0500.001-0.0010.074-0.0090.0170.0310.0150.0380.075-0.0010.0370.001-0.0010.006
is_seat0.0520.0080.0000.0150.0500.1620.0500.0530.0220.0870.0950.0420.0350.0000.1490.0260.1350.0490.0740.2020.0090.0000.0000.0261.0000.0530.0790.0250.1480.0650.0200.0740.0230.0120.1570.0340.0220.0000.0000.002
leg_arrival_hour-0.0170.010-0.0030.0090.0340.127-0.0140.0410.0110.021-0.0140.048-0.0160.0340.060-0.032-0.0290.1070.1420.041-0.004-0.0040.0070.0500.0531.0000.4390.0030.456-0.1330.0280.0540.0240.1130.341-0.0130.0750.000-0.002-0.007
leg_departure_hour-0.013-0.0100.0020.0110.0480.182-0.0150.049-0.0230.039-0.0600.0610.0310.0490.0880.0630.1540.0660.2310.050-0.005-0.0010.0000.0010.0790.4391.0000.0060.3300.1200.038-0.3020.0370.1350.4270.0450.0710.0030.001-0.004
leg_departure_month-0.0080.0040.0050.0050.0210.054-0.0060.0160.5280.018-0.0030.0640.0180.0700.0230.001-0.035-0.0100.0580.072-0.001-0.0030.006-0.0010.0250.0030.0061.0000.090-0.0140.018-0.0090.0140.0290.093-0.0100.0360.0050.0010.004
leg_destination_country_code0.0210.0310.0030.0310.0710.1790.0220.1140.0740.1000.0780.0980.0760.0640.1620.4230.5570.2230.4460.0680.0000.0030.0090.0740.1480.4560.3300.0901.0000.4060.0320.1580.0510.3910.1420.0490.5590.0180.0130.013
leg_duration_h0.0440.0220.0040.0120.0740.1680.0450.058-0.0510.0450.4430.0820.1010.0660.130-0.0840.4700.2450.3300.0750.0040.0080.000-0.0090.065-0.1330.120-0.0140.4061.0000.046-0.2100.0430.0720.3510.0760.1450.000-0.002-0.003
leg_first_leg_flg0.0110.0180.0000.0110.4640.0790.0090.0220.0440.0550.3500.4460.0500.0000.0160.0250.0580.1370.0570.1050.0130.0030.0050.0170.0200.0280.0380.0180.0320.0461.0000.0520.5720.1970.0590.5720.0570.0080.0100.000
leg_hours_to_departure0.004-0.001-0.0010.0060.0730.2650.0040.0600.0400.0430.0900.081-0.0540.0660.135-0.014-0.1880.0900.3260.084-0.000-0.0040.0040.0310.0740.054-0.302-0.0090.158-0.2100.0521.0000.0520.1010.733-0.0580.0520.006-0.001-0.004
leg_last_leg_flg0.0110.0130.0060.0000.4590.0770.0110.0200.0430.0510.3440.4410.0510.0060.0120.0230.0550.1260.0530.0970.0110.0000.0010.0150.0230.0240.0370.0140.0510.0430.5720.0521.0000.1890.0771.0000.0580.0000.0000.000
leg_number0.0060.0210.0020.0200.4820.0750.0080.0340.0910.0430.2450.2930.0490.0130.0260.0430.0800.5660.0750.0780.0000.0000.0050.0380.0120.1130.1350.0290.3910.0720.1970.1010.1891.0000.4180.1360.0490.0000.0060.005
leg_origin_country_code0.0000.0280.0030.0090.1340.2310.0120.1160.0810.0870.1140.1530.0640.0940.1530.3960.4950.2630.3980.0740.0080.0100.0000.0750.1570.3410.4270.0930.1420.3510.0590.7330.0770.4181.0000.0510.5810.0000.0000.000
leg_stopover_time_h0.0330.021-0.0020.0000.2650.0680.0350.015-0.0510.0300.2980.3120.0800.0060.030-0.0300.1260.1510.0900.050-0.000-0.0010.000-0.0010.034-0.0130.045-0.0100.0490.0760.572-0.0581.0000.1360.0511.0000.0640.0000.000-0.004
preffered_language0.0230.0270.0020.0030.1290.5920.0220.0870.0820.3480.1710.1440.1120.0040.0120.0260.0450.1080.0000.1350.0050.0000.0000.0370.0220.0750.0710.0360.5590.1450.0570.0520.0580.0490.5810.0641.0000.0000.0000.000
top_1_section0.0000.0040.0040.0000.0060.0000.0000.0000.0000.0000.0030.0030.0040.0000.0060.0080.0050.0030.0030.0000.0040.0000.7070.0010.0000.0000.0030.0050.0180.0000.0080.0060.0000.0000.0000.0000.0001.0000.5680.632
top_2_section0.0030.003-0.0000.0010.0040.0050.0050.003-0.0030.0010.0020.0030.0000.0060.000-0.0030.0030.0030.0020.000-0.001-0.0050.817-0.0010.000-0.0020.0010.0010.013-0.0020.010-0.0010.0000.0060.0000.0000.0000.5681.0000.098
top_3_section0.0030.007-0.0020.0040.0050.0100.0040.0000.0010.000-0.0030.000-0.0020.0080.0000.008-0.005-0.0070.0020.0000.003-0.0020.9350.0060.002-0.007-0.0040.0040.013-0.0030.000-0.0040.0000.0050.000-0.0040.0000.6320.0981.000

Missing values

2025-04-12T11:50:43.274869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-12T11:50:43.982272image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-12T11:50:45.180824image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idbooking_adult_countbooking_child_countbooking_destination_airport_codebooking_idbooking_infant_countbooking_leg_countbooking_marketbooking_origin_airport_codebooking_original_currencybooking_pax_countbooking_payment_methodbooking_reservation_monthbooking_sales_channelbooking_segments_countbooking_trip_typebooking_window_wcoupon_cabin_classcoupon_destination_airport_codecoupon_fare_basiscoupon_marketing_flight_numbercoupon_milescoupon_numbercoupon_origin_airport_codecoupon_rangecoupon_rbd_codeemailflight_coupon_idflight_leg_idis_seatleg_arrival_hourleg_arrival_timezone_codeleg_departure_hourleg_departure_monthleg_departure_timezone_codeleg_destination_airport_codeleg_destination_country_codeleg_duration_hleg_first_leg_flgleg_hours_to_departureleg_last_leg_flgleg_numberleg_origin_airport_codeleg_origin_country_codeleg_stopover_time_hpnrpreffered_languagerequest_dttmrequest_idclickedgraphic_designtop_1_sectiontop_2_sectiontop_3_section
010552910TBS638684002GEWAWGEL195582540167d4ebb9497a7031f0467571website2RT4economyWAWee702af56c9f42c49677ede02047afcd810249513342TBSSHb6460aef6f204ba8940329aa9f7f99cd4a0d5a2fe1974c1f8e8082afa8bdd72b7604138158590706Europe/Warsaw52Asia/TbilisiWAWNaN017402TBSNaN36540faefe514be47b38cea3be065583d7fen_gb2014-03-02T21:13:05.030Zee37548ce2a74b99b41d67f4d2a138fd0C630
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210766310BEG316431602PLSZZNaN11bb2b25a099d4a438425d276203a0c2c2website4RT0economyWAW76c8fafae85c47e0849a2b85b23dce0813035465073BEGSHfcd7f2fe3e034355bcc3fff350b5c92893fe4d004f074298ae540784f546692688648782564724020Europe/Warsaw132Europe/BelgradeSZZNaN607112BEGNaN-99996240c081718e4439a3174ac38a2c9c45en_gb2014-03-08T08:15:38.344Z9df9a894f4374dfba0d8811dd99c1b1e0C360
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